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---
license: mit
datasets:
- Replete-AI/code_bagel
language:
- en
tags:
- code
---
### Base_model
microsoft/Phi-3-medium-128k-instruct(https://huggingface.co/microsoft/Phi-3-medium-128k-instruct)
### Datasets
Replete-AI/code_bagel(https://huggingface.co/datasets/Replete-AI/code_bagel)
### Train Loss
### Train State
Trainable params: 27852800 || all params: 13988090880 || trainable%: 0.1991
Total Training Duration：69h18m17s
{
"epoch": 0.9999679800589659,
"total_flos": 1.446273483573748e+20,
"train_loss": 0.44412665014957775,
"train_runtime": 249497.725,
"train_samples_per_second": 13.018,
"train_steps_per_second": 0.102
}
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 1
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- gradient_accumulation_steps: 16
- total_train_batch_size: 128
- total_eval_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 1200
- num_epochs: 1.0
### I personally fine-tuned the largest dataset, which took the most time.
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